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@ferqui
Created May 13, 2021 08:08
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Comparison of the FPGA implementation of R-STDP with a simulation done in Brian2.
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{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# Reward Modulated STDP implementation on FPGA"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "from brian2 import *\nimport pandas as pd",
"execution_count": 1,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "INFO Cache size for target \"cython\": 1227 MB.\nYou can call \"clear_cache('cython')\" to delete all files from the cache or manually delete files in the \"C:\\Users\\ferqui\\.cython\\brian_extensions\" directory. [brian2]\n"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Simulation parameters"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Parameters\nsimulation_duration = 60 * ms",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "## Neurons\ntaum = 10*ms\nEe = 0*mV\nvt = -54*mV\nvr = -60*mV\nEl = -74*mV\ntaue = 5*ms\n\n## STDP\ntaupre = 16*ms\ntaupost = taupre\ngmax = 1.0\ndApre = .125\ndApost = -.25\n\n## Dopamine signaling\ntauc = 256*ms\ntaud = 1*ms\ntaus = 1*ms\nepsilon_dopa = 1",
"execution_count": 3,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "First we define the input spikes times and adjust the timestep of the simulation to the one used in the FPGA."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "start_scope()\ndefaultclock.dt = 0.0625*ms\n\n## Stimuli section\ninput_indices = array([0, 0, 0, 0, 1, 1,\n 1, 1, 1, 1, 1, 1])\ninput_times = array([ 1, 6, 16, 26, 7, 17,\n 19, 21, 23, 25, 27, 29])*ms\ninput = SpikeGeneratorGroup(2, input_indices, input_times)\n\nneurons = NeuronGroup(2, '''dv/dt = (El - v) / taum : volt\n dge/dt = -ge / taue : 1''',\n threshold='v>vt', reset='v = vr',\n method='exact')\nneurons.v = vr\nneurons_monitor = SpikeMonitor(neurons)\n\nsynapse = Synapses(input, neurons,\n model='''s: volt''',\n on_pre='v += s')\nsynapse.connect(i=[0, 1], j=[0, 1])\nsynapse.s = 100. * mV",
"execution_count": 4,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Now we define the R-STDP synapse that connect both neurons. In order not to alter the pre- and post-synaptic spikes, the weight of this synapse does not affect the dynamics of the neuron."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "## STDP section\nsynapse_stdp = Synapses(neurons, neurons,\n model='''mode: 1\n dc/dt = -c / tauc : 1 (clock-driven)\n dd/dt = -d / taud : 1 (clock-driven)\n ds/dt = c * d / taus : 1 (clock-driven)\n dApre/dt = -Apre / taupre : 1 (clock-driven)\n dApost/dt = -Apost / taupost : 1 (clock-driven)''',\n on_pre='''ge += s\n Apre += dApre\n c = clip(c + Apost, -gmax, gmax)\n ''',\n on_post='''Apost += dApost\n c = clip(c + Apre, -gmax, gmax)\n ''',\n method='euler'\n )\nsynapse_stdp.connect(i=0, j=1)\nsynapse_stdp.mode = 0\nsynapse_stdp.s = 0.986618041992188\nsynapse_stdp.c = 0\nsynapse_stdp.d = 0\nsynapse_stdp_monitor = StateMonitor(synapse_stdp, ['s', 'c', 'd', 'Apre', 'Apost'], record=[0])",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "## Dopamine signaling section\ndopamine_indices = array([0])\ndopamine_times = array([20])*ms\ndopamine = SpikeGeneratorGroup(1, dopamine_indices, dopamine_times)\ndopamine_monitor = SpikeMonitor(dopamine)\nreward = Synapses(dopamine, synapse_stdp, model='''''',\n on_pre='''d_post += epsilon_dopa''',\n method='exact')\nreward.connect()",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Simulation\nrun(simulation_duration, report='text')",
"execution_count": 7,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "Starting simulation at t=0. s for a duration of 60. ms\n60. ms (100%) simulated in < 1s\n"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Read FPGA data"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df=pd.read_csv('dopa_decay1.csv',skiprows=1)\nfontsize = {'fontsize': 15}\ndata=df['HEX.2'].to_numpy()\ndopadata = []\nfor x in data:\n dopadata.append(float(int(x[0],16)) + float(int(x[1:],16))/65536)\ndopadata = np.array(dopadata)\ntime = arange(1024)*0.0625\nplt.plot(time,dopadata)",
"execution_count": 8,
"outputs": [
{
"data": {
"text/plain": "[<matplotlib.lines.Line2D at 0x1a805f77fc8>]"
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qi/luc+HMvegHfuLEqLTcDHRVstBTWt44NgrAdydeXvpiJL2Kga7K5h+fw+hQP5vWDvPdQwa6tNwMdFXTwZMUL9s4ytMGurTsDHR13WUbR/nuxMtMT/sYXWk5Geiq7BxzokAz0I+dnOLZF33qorScDHRV0smY+7LWxOg+2y7SsjLQVdkCA3Qu29gM9Kd//NLSFyPpNANdlXSyu9yFo0NcdMEQew++uPQFSTrNQNeSuGLLOr41/kLdZUivKQa6KjvXStEZV25Zy/6Joxx5xf1FpeVioKuS7GhatDlCB3j8h0eWsBpJ7Qx0LYkrtqwF4JFnXqi3EOk1xEBXZQs3XGDdyCDbNqzmmwdeWOpyJLUY6Kqkk7tcZrz9ja/jG/ufZ3JqeukKknSaga7KOpgTBeBdl43x0olJ73aRlomBrkqqjND/yZsuJAK+9vRzS1eQpNMMdC2Z9asH+fnNaw10aZkY6FqEDnsuwHVv3sg/HjjMj474oC5pqRnoqqTqA3Hff+XFZMIDjx5cknoknWGgq7JOJ0WhuSXdz29eyxcfMdClpWagq5KsMivacuNVF/PYD4+4alRaYga6ltyv7djK6sEGd39tf92lSCuaga7KKnRcAFi7aoBff9sl/OWjzzJ++NiS1CTJQNcy+ei7t9HfF/y3v/p23aVIK5aBrmVx8bpV/Otr38QDjz7L3+/zvnRpKRjoqiSz2l0u7W79xTexbcNq/t2fPcJzL5/obmGSDHQtn+GBBnd+4GqOvHKKf/XZPbx8YrLukqQVxUBXZVF5WvSMyy++gD+66S08On6ED979DVeQSl1koKuSTncsOpedP/d6Pv3Bq3n6xy9xwye/xp89dIDp6fP/utJrXUeBHhE7I+KpiNgXEXfMcT4i4pOt849GxNXdL1UryXt/9vV88bZ3sm3Daj7++ce47g++wv/86n4OPO9tjdJi9S90QUQ0gDuB9wDjwEMRcX9mPtF22fXA9tbbLwCfab3XCrTYSdHZtl+0hs/d+g52P/Yj/vj/7uf3dj/J7+1+kq2vW8XPblrLmzetYfO6Vbx+7TAb1wwzOtzP6GA/I0MNBhr+41KabcFAB64B9mXmfoCIuA+4EWgP9BuBz2ZzXfjXI2JdRGzKzGe7XfDffWeC//rAEwtfqCXxg+eP8aax0a59vYjgfVds4n1XbOIHzx/ly08e4uEDh3ni4It8ae+P5v28wf4+hhp9NBpBf1/Q6Av6+/pa72Pev3RinhNd+jtK6sivv20rH333G7v+dTsJ9M3AM22vxzl79D3XNZuBVwV6RNwC3AJwySWXVK0VgNGhfrZf1L1AUTXbLxrl13ZsXZKv/YYLV/ORd23jI2wD4PipKQ69eIJnj7zCoZdOcOzkJC+fmOLYiUlePjnJqclkanqayelkajpPvz81NT13p3+eNn035gWkKjaMDi3J1+0k0OcavMz+DejkGjLzLuAugB07dizqt+itb1jPW9/w1sV8qnrM8ECDSy4c4ZILR+ouReoJnTQix4H2IdkWYPazUDu5RpK0hDoJ9IeA7RGxLSIGgZuA+2ddcz/wodbdLm8HjixF/1ySNL8FWy6ZORkRtwMPAg3gnszcGxG3ts7vAnYDNwD7gGPAh5euZEnSXDrpoZOZu2mGdvuxXW0fJ3Bbd0uTJFXhzbyStEIY6JK0QhjokrRCGOiStELEYnZx78o3jpgAfrDIT98A9PK2N9Zfn16uHXq7/l6uHcqp/w2ZOTbXidoC/XxExJ7M3FF3HYtl/fXp5dqht+vv5dqhN+q35SJJK4SBLkkrRK8G+l11F3CerL8+vVw79Hb9vVw79ED9PdlDlySdrVdH6JKkWQx0SVohei7QF9qwujQRcU9EHIqIx9uOvS4i/joinm69X19njfOJiK0R8bcR8WRE7I2Ij7WO90r9wxHx/yLiW636/0vreE/UD809fSPimxHxQOt1L9X+/Yh4LCIeiYg9rWM9UX9rG83PRcS3Wz//7+iF2nsq0Ns2rL4euBy4OSIur7eqBd0L7Jx17A7gy5m5Hfhy63WJJoF/n5k/A7wduK3137tX6j8BXJeZVwJXATtbz+vvlfoBPgY82fa6l2oH+KXMvKrt/u1eqf+PgC9l5puBK2n+Pyi/9szsmTfgHcCDba8/AXyi7ro6qPtS4PG2108Bm1ofbwKeqrvGDv8cXwTe04v1AyPAP9LcD7cn6qe589eXgeuAB3rtZwf4PrBh1rHi6wcuAL5H66aRXqq9p0bozL8Zda+5KFs7OrXeb6y5ngVFxKXAW4Bv0EP1t1oWjwCHgL/OzF6q/w+B/wBMtx3rldqhua/w/4mIh1sbxENv1P9GYAL4X612190RsZoeqL3XAr2jzajVXRExCnwe+LeZ+WLd9VSRmVOZeRXN0e41EfFzNZfUkYj458ChzHy47lrOwzsz82qaLdLbIuKf1l1Qh/qBq4HPZOZbgKOU2F6ZQ68F+krZjPrHEbEJoPX+UM31zCsiBmiG+Z9k5hdah3um/hmZ+QLwFZrzGb1Q/zuB90fE94H7gOsi4n/TG7UDkJkHW+8PAX8OXENv1D8OjLf+NQfwOZoBX3ztvRbonWxY3QvuB36z9fFv0uxNFyciAvhj4MnM/B9tp3ql/rGIWNf6eBXwz4Bv0wP1Z+YnMnNLZl5K8+f8bzLzX9IDtQNExOqIWDPzMfArwOP0QP2Z+SPgmYj46dahXwaeoAdqr72Jv4gJixuA7wDfBf5j3fV0UO+fAs8Cp2j+zf/bwIU0J7uebr1/Xd11zlP7u2i2tB4FHmm93dBD9V8BfLNV/+PA77SO90T9bX+OazkzKdoTtdPsQ3+r9bZ35ne1h+q/CtjT+tn5C2B9L9Tu0n9JWiF6reUiSZqHgS5JK4SBLkkrhIEuSSuEgS5JK4SBLkkrhIEuSSvE/wfljZ50+OAoaAAAAABJRU5ErkJggg==\n",
"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df=pd.read_csv('weights.csv',skiprows=1)\nfontsize = {'fontsize': 15}\ndata=df['HEX.2'].to_numpy()\nweights_update = []\nfor x in data:\n weights_update.append(float(int(x[0],16)) + float(int(x[1:],16))/65536)\nweights_update = np.array(weights_update)\ntime = arange(1024)*0.0625\nplt.plot(time,weights_update)",
"execution_count": 9,
"outputs": [
{
"data": {
"text/plain": "[<matplotlib.lines.Line2D at 0x1a805a5a1c8>]"
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df=pd.read_csv('e_trace.csv',skiprows=1)\nfontsize = {'fontsize': 15}\ndata=df['HEX.2'].to_numpy()\ne_trace = []\nfor x in data:\n e_trace.append(float(-(int(x[0],16) & 0x2) | (int(x[0],16) & 0x1)) + float(int(x[1:],16))/65536)\n #weights_update.append(float(int(x[0],16)) + float(int(x[1:],16))/65536)\ne_trace = np.array(e_trace)\ntime = arange(1024)*0.0625\nplt.plot(time,e_trace)",
"execution_count": 10,
"outputs": [
{
"data": {
"text/plain": "[<matplotlib.lines.Line2D at 0x1a805282a88>]"
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df=pd.read_csv('weight_plus.csv',skiprows=1)\nfontsize = {'fontsize': 15}\ndata=df['HEX.2'].to_numpy()\nweight_plus = []\nfor x in data:\n weight_plus.append(float(-(int(x[0],16) & 0x2) | (int(x[0],16) & 0x1)) + float(int(x[1:],16))/65536)\n #weights_update.append(float(int(x[0],16)) + float(int(x[1:],16))/65536)\nweight_plus = np.array(weight_plus)\ntime = arange(1024)*0.0625\nplt.plot(time,weight_plus)",
"execution_count": 11,
"outputs": [
{
"data": {
"text/plain": "[<matplotlib.lines.Line2D at 0x1a805a23708>]"
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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HMnpIZ5Zs3Md78zcG4CecEOgWPVSvE/jFqRmMm1Fmu3UQSnlCg94iZ3VqQ/8OiTz9xSrLNjyrHowNTFReXJBOQUY8/5y6IiAbnp2YXtkUUV+9+jckBP7x6fIm+XlK+ZMGvQ9OdB94HzYiwgMX5LP34BGe+8q63RIDFZMhIcIfL8xny75DvDDd/1sjNPVAdmp8FCMH5fLxos0Ur7N+xpRS3tCg94GvA4L56XFc2iuDl2eVsW7nAf8V5iETqL4bl9OyExjaNZUx36xh2z7/bugW6AVTdbl1UHvaxEby6MfLbDFjSilPadD7gS9hc+95nQgPDeEfnzR9l0BTRNV9Qztz5Ogx/u8z/279EKjdK08mJjKM357Xifnr9/DRIl1EpYKHBr0P/DHzIyUuilFn5PLJki3MLWvi3RID26AHoF1iDNf3y+bN4g0s27TP/z+gKZv0wCW9MuiSFsc/PlnOoSPWHaGolDc06H3grxbxLQPbkxYfxZ8nL+VoE+6rYjBNMph5x9l5tGoRzp8nL/Vbl0dTzaOvLTREeHBYFzbuOWjJtsxKNYYGvQ/8tYNii4hQHhjWhaWb9vF6E5+/2hRBGR8dzughnZlTtosPFvjpbHgLu8hP75DEBd1See6r1TrdUgUFDXofHM8aP6TlsG5p9O+QyD+nrmDnD020p7ufNzU7mcsLMynIiOevU0rY74cVplYMxrr747B8QkR45KNl1hSglBc06P3AH1kjIvzl4lOoqDzKY000V7spJ46EhAgPD+/Kjh8O+2V3S2/OAgiE9FYtuGNwBz5btpWvVli7lYVSDdGg94Ev8+jr0qFNLDcPzOHNovIm2d3SYJo0KAsyW3HFaVm8PKvMb3vyW9WiBxgxoD3tk2P48+SlOjCrbE2D3if+bxLfeXYeqXFRPPTBkoAPzDZl102N0ed3IjYqjAc/WOLTwKw/D01prIiwEP5y8Sms21mhA7PK1jTofRCIudwxkWE8MKwLSzbu43UHbo3bOiaC0ed3Zk7pLiYvbPzArBXz6OsyMC9ZB2aV7WnQ+yBQA4IXdk/j9NxE/vnp8oAOzAZyr5uTufw018Dsx40fmLV6MNadDswqu9Og9wN/93OLCA8PD/zArFWr+ENdA7Pb/TAwa9VgrDv3gdkvl2+1uhylfkKD3geBDEr3gdnAHeBhLIvJgsxWXNk7i3EzS1lcvtfr+41d+m5cRgxoT16bljz4/lIOHK6yuhylfkSD3gfHF0wFKGzuHtyRrIRo7ntnUcBmdVjZ9fH7IZ1JahnJ6HcWeX0Ai932FIsIC+Efl3Rj456Dft/XRylfadD7wPuDBL3TIiKUv/+iG2U7K/wy97w2K2bduItvEc7Dw7tSsnkfYxs5a8UmDXoAerVL4Jq+Wbw8q5SFG/ZYXY5Sx2nQ+8AEOumB/h2SuKwwgxemr2XJRu+7OE7GYH0f95CuqQztmsq/vljFmu0/eHyfv9cw+MvoIZ1Jjo3kvncXW3pMpFLuNOj9INBh+cAF+STERPD7dxZR5cfwsMue6n8ZfgpRYSHc/85ir8+YtVfMQ1xUOH+5uPpTyv+ml1pdjlKAh0EvIkNEZIWIrBaR++p4vbOIfCcih0Xkt7VeKxORxSKyQESK/FW4HTTVop346HAevvgUlm7axwt+DI/q6ZV+e7tGaxMbxR+H5TOnbJfHaweO715pg/prG9I1lfNPSeHpaSu9+pSiVKA0GPQiEgo8DwwF8oErRSS/1mW7gDuBJ+p5m7OMMT2MMYW+FGs7ftiP3lNDu6UdD4/SHf47jcouOXlpYQb9OyTyj0+Ws3nvwQavt8mHkXo9MrwrUeGh/O6thU269bRSdfGkRd8bWG2MWWuMqQQmAcPdLzDGbDPGzAWsOeXaIk3QRf8jDw/vSkRYCPe9s8jrLo66GLs06anua//7z7tTdewYD77f8PYIdlowVZc2cVH85eJTmLd+D+NmaheOspYnQd8W2OD2uNz1nKcM8JmIFIvIyPouEpGRIlIkIkXbt2/34u2t09QDgilxUTxwQRe+L93Fq37Yt756MNY+shKjuffcTkwr2cb7Czae9Fp/nQUQSMN7pHNOlxT+OXWFduEoS3kS9HX9TfKmOdnfGNOT6q6f20RkUF0XGWPGGmMKjTGFycnJXrx983L5aZkM6pjM36aU+NyFY5fBWHc3DcihV7vWPPTBUo+6cOzaoofqBsDffl7dhTP67UXahaMs40nQlwOZbo8zAI93ozLGbHJ93Qa8R3VXkCNYMSAoIjx+SXciQkO4980FPs/CsVtQhoYI/3dpAVVHDaPfXlTv/4yCJTLbxEXx54vzKV63W7twlGU8Cfq5QJ6I5IhIBHAFMNmTNxeRGBGJrfkeOA9Y0thi7caqVfip8VE88rOuzFu/h//6uD2uzXIegOykGB4Y1oXpq3bw6uy6u6hs+GGkXj/r0ZZzuqTw+NQVftuHXylvNBj0xpgq4HZgKlACvGmMWSoio0RkFICIpIpIOXAP8EcRKReROCAFmCEiC4E5wMfGmE8D9cs0NSsHBC8uSGdYtzSenraSZZv2Neo9qlfG2jHq4eo+Wa4uquX1dFHVfJqyZ/3uRIR/XNKNuKgw7po0n8NVekiJaloezaM3xkwxxnQ0xuQaY/7qem6MMWaM6/stxpgMY0ycMaaV6/t9rpk6Ba4/p9Tc6zxNHzYiwiM/60qr6AjueXNBo8LDWLipWUNquqjCQ4V7TtJFZdf6a0tqGcljl3Rn+Zb9uheOanK6MtYHVg9mJsRE8Ngl3Vi+ZT+PfbLC6/vt3v2RGh/Foz/vxvz1e3j2y9U/es004RoGfxncJYWr+2TxwvS1zFq9w+pyVDOiQe8DO8zlPrtzCtf3a8dLM0u9PqTa6k3NPHFxQTq/6NmWZ79cxdyyXceft/n/o+r1wLAu5CTGcO9bC9lb0ayWnSgLadD7wiZbot9/QRc6p8by2zcXsm3/Ia/utfM89BoPD+9KZkI0d09acDwcTwyE279+d9ERYTx9RQ+27z/MH95bbPmnQtU8aND7wNhkQDAqPJRnrzyVHw5Xce+bCz1eNWsw1v9fygMtI8P41xWnsnXfIf7wfnU42nmvm4Z0z2jFPed15OPFmx15LrCyHw16P7BD1uSlxPLghflMX7WDF2d4Nl/bBEfOA9Aj0xWOizbzVlH58eeDpf7aRg3KZWBeEg9/uIySzY2bNaWUpzTofWC3T91X98nivPwUHp+6nEXlexq83mblN2jUoFxOz03kT5OXsmZb9ZTLYGzRA4SECE9d3oO4FuHc/vo8Kir1+EEVOBr0PrDbzA8R4bFLupPcMpJfvzav4cG+IBiMdRcSIjx5WQ9aRITywPuLrS7HZ0ktI/nX5T1Yu+MAD32w1OpylINp0PvgxO6V9knL1jERPHd1T7buO8S9by1osL/eTrV7IjU+iqcu78Ge4/8TC676azu9QxJ3nNWBt4vLeXdeecM3KNUIGvQ+CPTh4I3VM6s1f7igC9NKtjF2ev1bJBiM7Wr3xBkdk7ntrFwAIsKC8Beo5c7BefTOSeCP7y9h1VbdIkH5nwa9Q91wejbDuqXxz6kr+H7tzjqvsdsYgzd+c05HxlzTi9Nzk6wuxWdhoSE8e+WpREeEceuEYvYf0vn1yr806H1g55ys2V8lKyGaOybOZ/v+wz+5xkbnjngtLDSEIV1TiQoPtboUv0iJi+K5q05l3a4KfvvWQp1fr/xKg94HdhuMrS02Kpx/X92TfYeOcNvr8zhSa78YY0zQ9dE7Wd/2idw/tDNTl27lP9+ssboc5SCOD/qXZ5byzcpAnVhl/1OOuqTF8dgl3ZlTuouHP1z2k9ft+j+p5urmATlc2D2NJ6auYMYq3Q9H+Yfjg/75r9dw64Qilmzc6/f3tnuLvsbwHm25dVB7Jsxex0S3lZjaOWA/NVNkO7RpyR0T51G+u8LqkpQDOD7ojYFDR45xy/iiOvupm4vRQzozqGMyD32whCLX5mDaDWxPMZFh/PfaQqqOGkaOL9bFVMpnjg96MPTMasXuikp+9WqxXw99sMPulZ4KDRGeveJU2rZqwahX57F570HXYGwQFN8M5STF8MxVp7J8yz5+80bD6yGUOhnHB70x1f3U//xlAUXrdvOnD5b6bUZDsO2gGB8dztjrCjlYWcWtE4o5fORokFTePJ3VqQ0PDMtn6tKtPPm5HlaiGs/xQQ/VLe6LCtK57axcJs3d4PGmXw0Jxh0UO6bE8tTlPVhUvpflW/YHVe3N0U39s7nitEye+2o178/faHU5Kkg5PugNJ1rc957biaFdU/nrlBI+XbLF9/cO0k/T552Syu/O7wQE+wYCziciPDy8K31yEhj9ziLmrd9tdUkqCDk/6N3SuGbHwIKMVtz9xnwWbNjjl58RjGH56zNz+fWZuZx/SqrVpagGRISFMOaaXqTGRTFyfLHOxFFec37Q8+OulajwUP53fSHJsZGMeGUuG3Y1/i9NMA3G1iYijB7SmSt6Z1ldivJA65gIXry+kMNVR7lh3Fz2VFRaXZIKIs4P+joO10hqGcm4G06jsuoYN748l70HG7e3yIlPC0GY9Cro5KXEMvbaQtbvrGDEK0UcOuK/GWTK2Rwf9FD3FMIObWL577WFrNt5wOdpl8HYolfBqV9uIk9eXj2D7O5JCziq0y6VBxwf9CebStkvN5HHLunOrDU7+c0b+pdGBYcLu6fz4IX5fLp0C3/50H/ThZVzhVldQKA19FfgFz0z2HWgkkc/LiG+xWL+9vNuHi8iOjGPXqmmdfOAHLbsPcgL00tJi2/Br87MtbokZWOOD3pPjssbMbA9eyqO8NxXq2kdHcHoIZ09fOuaefQa9arp3T+0C1v2HeaxT5fTJjaSS3plWF2SsinHB737PPqTufe8juyqqOTfX6+hdXQEtwxq3/B7a4teWSgkRHji0u7s/OEwo99ZRExkKEO6plldlrIhx/fRg2eDpSLCI8O7Mqx7Gn+dUsKbRRsavCdYdq9UzhUZFsoL1xVSkBHPHRPn89XybVaXpGzI8UFffbiGZ0JDhKcu68HAvCTue2eRX1bPKhVoMZFhjLuxN51SYxn1ajGz1ug+9urHPAp6ERkiIitEZLWI3FfH651F5DsROSwiv/Xm3kDzdj5CzSrEgsxW3P76PD5bWn/Yn5hFr016Za34FuGMv6kPWQnRjHiliOJ1ulWCOqHBoBeRUOB5YCiQD1wpIvm1LtsF3Ak80Yh7A8p4MBhbW0xkGK/c1JuubeO57fV5fL5saz3vHXybminnSoiJ4LURfWgTG8kN4+YE5LAdFZw8adH3BlYbY9YaYyqBScBw9wuMMduMMXOB2ktMG7y3KTRmVkxcVDjjb+5Nfno8v36tmGl1hL3OXlZ20yYuitdu6UtcVDjXvvi9hr0CPAv6toD7yGS56zlPeHyviIwUkSIRKdq+3X9nvBo876OvLS4qnPE39SY/LY5fvVbMFyW1wl6TXtlQ21YteP2WPrQID+WqF2az0E+b96ng5UnQ15WTnkacx/caY8YaYwqNMYXJyckevn3DfF00GN8inPE396FLWhy/enVenbMatOtG2U27xBjeuLUf8dHhXP2/748fH6maJ0+CvhzIdHucAWzy8P19udcvDPg80T2+RTgTbupDp9RYbp1QfLzPXhdMKTvLTIjmzVv70SY2kutemqOzcZoxT4J+LpAnIjkiEgFcAUz28P19udc/jH9mxcRHh/PqzX3okh7HqFeL+WDBRl0wpWwvLb4Fk27tS9tWLbhx3Fy+Wem/blEVPBoMemNMFXA7MBUoAd40xiwVkVEiMgpARFJFpBy4B/ijiJSLSFx99wbql6mPvxrc8dHhvDaiD6dlt+buNxbwnutoN23QKztrExvFpJF9aZ/cklteKapzYoFyNo/m0RtjphhjOhpjco0xf3U9N8YYM8b1/RZjTIYxJs4Y08r1/b767m1KvgzG1qVlZBgv39ibszu14ftS7fdUwSGxZSQTb+lDl7TY459IVfPRDFbG+v89o8JDGXNtLy4uSCcsRIiOcPyWQcoBWkVHMGFEH3q1a81dkxYw9ts1usVxM+H8oCcwXSvhoSH864oezHngHOJbhPv/BygVAHFR4bxyU2+GdUvjb1OW88hHJRzTcxgcz/FN0eq9bgLTiS4iJMREBOS9lQqUqPBQnr3yVNrERfLSzFK27j/Ek5cVEBkWanVpKkAcH/Sgg6VK1RYSIjx0YT5p8VH8bcpydv5wmLHXFRIXpZ9OnahZdN0opX5KRBg5KJenL+9B8brdXDbmOzbvPWh1WSoAnB/0Rue5K3UyPzu1LeNu6E357oNc/NxM5q3XnS+dxvFBD2jfjVINGJCXxLu/Pp3oiFCu+O9s3vLg4B0VPBwd9Me3Eba4DqWCQceUWD64rT+9cxL43duLePjDZVQdPWZ1WcoPHB30NbRBr5RnWkVH8PKNp3FT/xxemlnKjS/PZU9FpdVlKR85Ouh1LYhS3gsLDeGhi/J5/Jfd+X7tLoY/P5OVW/dbXZbygbOD3vVVj/pTynuXFWYycWRfKiqP8vPnZ570WE1lb84Oej3qTymf9GrXmg9vH0CHNi0ZOaGYv368jMoq7bcPNo4O+hqa80o1Xmp8FG/c2o/r+rXjhemlXDpmFut3VlhdlvKCo4Neu+iV8o+o8FAeHt6VMdf0pHTHAYY9M52PFjXpGULKB84O+pqDQbRJr5RfDOmaxsd3DiQvpSW3vz6f+99dxMHKo1aXpRrg7KDXo/6U8rvMhGjeuLUfvzozl4lzNjD8+Rms0lk5tubsoNe+G6UCIjw0hN8P6cz4m3qz60AlFz03gwnflen+9jbl6KCvoQ16pQJjUMdkptw1kNOyE3jwg6Vc99Ic3RjNhppF0CulAqdNbBTjb+rNoz/rSlHZbs576lvem1+urXsbcXTQHx+M1QmWSgWUiHBN33Z8evdAOqXE8ps3FjJyQjFb9x2yujSF04MeXTClVFNqlxjDG7f244ELuvDtyu2c8+Q3TJyzXlv3FnN20B9v0SulmkpoiHDLoPZMvXsQp6THcf+7i7nyhdmU7ThgdWnNlqODvoa26JVqetlJMUy8pS9//0U3lm7cx/lPf8szX6zicJXOu29qjg56/bColLVEhCt7Z/H5PWdwTn4KT36+kiFPT+fbldutLq1ZcXbQHz94RJv0SlkpNT6K56/qyfibemOM4bqX5nDba/N0KmYTcXbQu75q141S9jCoYzKf3j2Ie87tyLSSrZz9xDc888UqDh3R7pxAcnbQa9+NUrYTFR7KnYPzmHbPGZzZKZknP1/J2U98zeSFm3R2ToA4OuiVUvaVmRDNf67pxaSRfWkVHcGdE+fzyzHfMW/9bqtLcxxnB/3x3Su170Ypu+rbPpEP7xjA45d0Z93OCn7x71mMHF/E6m26UZq/ODrojy+YsrgOpdTJhYYIl52WyTe/O5N7z+3IrDU7Oe+pbxn99kI27dEBW195FPQiMkREVojIahG5r47XRUSecb2+SER6ur1WJiKLRWSBiBT5s/iG6H70SgWXmMgw7hicx7ejz+LG/jm8P38TZz7xNY9+tIzt+w9bXV7QajDoRSQUeB4YCuQDV4pIfq3LhgJ5rj8jgf/Uev0sY0wPY0yh7yV77sTh4EqpYJIQE8GDF+bz5W/P4KLu6bw0s5SBj3/Jox8tY9t+3T/HW5606HsDq40xa40xlcAkYHita4YD40212UArEUnzc61KqWYmo3U0/3dZAdPuOYMLuqUxblYZAx/7ioc/XMY23TDNY54EfVtgg9vjctdznl5jgM9EpFhERtb3Q0RkpIgUiUjR9u3+WTV3fMGU9t0oFdTaJ7fkyct68MU9Z3BRQTqvfFfGwMe/4k8fLGHDLj2ovCGeBH1dKVl7suvJrulvjOlJdffObSIyqK4fYowZa4wpNMYUJicne1BWw3TBlFLOkp0UwxOXFvDlvWcwvEc6r89Zz5lPfM0dE+ezZONeq8uzLU+CvhzIdHucAdQ+/r3ea4wxNV+3Ae9R3RXUJHT3SqWcqV1iDI//soDpo89mxIAcvl6+jQufncFVL8zm6xXbdOFVLZ4E/VwgT0RyRCQCuAKYXOuaycB1rtk3fYG9xpjNIhIjIrEAIhIDnAcs8WP9SqlmLDU+ivsv6MLM+8/m/qGdWbP9B24YN5chT0/n9e/XU1FZZXWJthDW0AXGmCoRuR2YCoQCLxljlorIKNfrY4ApwAXAaqACuNF1ewrwnquPPAx43Rjzqd9/i/pqR+dXKtUcxEWFc+sZudzYP4fJCzfx4oxS/vDeYv7+SQmX9srk2n7tyEmKsbpMyzQY9ADGmClUh7n7c2PcvjfAbXXctxYo8LHGxtOuG6WalYiwEH7ZK4NLeraleN1uXvluHeO/K+OlmaUM6pjM9f3acWanNoSGNK9U8Cjog5UOxirVPIkIhdkJFGYnsG1YFybO2cBr36/j5leKyExowWW9MrmkVwbprVpYXWqTcHbQ6+HgSjV7beKiuOucPH59Vi6fLd3KhNll/N/nK3ly2koGdEjissJMzs1PISo81OpSA8bRQa+UUjXCQ0MY1j2NYd3TWL+zgrfnlfN20QbumDif+BbhDO+RzmWFmZySHue4tTeODvrjm5o569+ZUspHWYnR3HNuR+4anMesNTt4q6icSXM3MP67dXRMacnFBelc2D2dbIcM4Do76HUwVil1EqEhwsC8ZAbmJbO34giTF21i8oKNPPHZSp74bCUFGfFcVJDOsO5ppMUHb3++s4Pe9VVb9EqphsRHh3Nt33Zc27cdm/Yc5KNFm/hw4WYe/biERz8uoXd2Ahf1SGdo11SSWkZaXa5XnB30ujpOKdUI6a1aMHJQLiMH5VK64wAfLtzE5IWbePD9JfzpgyX0ateac/NTOKdLCu2TW1pdboMcHfQ1dNaNUqqxcpJiuHNwHnec3YEVW/czZfEWpi3byt+mLOdvU5aTmxzDOfkpnNslhVOzWttyjr6jg97ohvRKKT8RETqnxtE5NY57zu1I+e4KvijZxrSSrbw4vZT/frOWxJgIzu7chnPyUzg9N5HYqHCrywYcHvQ1NOeVUv6W0Tqa60/P5vrTs9l36AjfrNjOtJKtfLp0C28VlxMaIvTIbMWADkkMzEuiILMV4aHWnN7q6KA3eji4UqoJxEWFc1FBOhcVpHPk6DGKynYzY/V2ZqzawTNfruJfX6yiZWQYfdsnMKBDEgPykslNjmmybHJ20P9k23yllAqs8NAQ+uUm0i83kd+dD3sqKvluzU6mr97BzNU7mFayDYC0+Cj6u1r7vXMSAjp909FBX0Pb80opq7SKjmBotzSGdqs+XXXDrgqmr9rBjNXb+XzZVt4uLgcgo3UL+uQk8s9fdifEzwO6jg56o7sUK6VsJjMhmqv6ZHFVnyyOHjOUbN7HnNJdzC3bxZZ9B/0e8uD0oHd91aBXStlRaIjQtW08XdvGc9OAnID9HGuGgJvI8cPBtfNGKdWMOTvorS5AKaVswNFBX0O7bpRSzZmjg163ulFKKYcHPcf3o9cmvVKq+XJ00GuLXimlHB70NbQ9r5Rqzhwd9DqPXimlnB70x48S1KRXSjVfzg56PRxcKaUcHvQ6GKuUUs4O+hraoFdKNWeODnrdvVIppZwe9OihsUop5eyg1z56pZTyLOhFZIiIrBCR1SJyXx2vi4g843p9kYj09PTepqBdN0qp5qzBoBeRUOB5YCiQD1wpIvm1LhsK5Ln+jAT+48W9Aac5r5Rqzjw5Yao3sNoYsxZARCYBw4FlbtcMB8ab6pM+ZotIKxFJA7I9uNdvLnp2BoeOHD3++FBV9fe6qZlSqjnzJOjbAhvcHpcDfTy4pq2H9wIgIiOp/jRAVlaWB2X9VG5yDJVHj/3oudOyE+iZ1apR76eUUk7gSdDX1RyuPcxZ3zWe3Fv9pDFjgbEAhYWFjRpGffqKUxtzm1JKOZonQV8OZLo9zgA2eXhNhAf3KqWUCiBPZt3MBfJEJEdEIoArgMm1rpkMXOeafdMX2GuM2ezhvUoppQKowRa9MaZKRG4HpgKhwEvGmKUiMsr1+hhgCnABsBqoAG482b0B+U2UUkrVSYwNVxUVFhaaoqIiq8tQSqmgISLFxpjCul5z9MpYpZRSGvRKKeV4GvRKKeVwGvRKKeVwthyMFZHtwLpG3p4E7PBjOU0pmGsHrd9KwVw7BHf9dqm9nTEmua4XbBn0vhCRovpGnu0umGsHrd9KwVw7BHf9wVC7dt0opZTDadArpZTDOTHox1pdgA+CuXbQ+q0UzLVDcNdv+9od10evlFLqx5zYoldKKeVGg14ppRzOMUFvh0PIvSEiL4nINhFZ4vZcgoh8LiKrXF9bW1ljfUQkU0S+EpESEVkqIne5ng+W+qNEZI6ILHTV/xfX80FRP1Sfxywi80XkI9fjYKq9TEQWi8gCESlyPRdM9bcSkbdFZLnr70A/u9fviKC3yyHkXnoZGFLrufuAL4wxecAXrsd2VAXca4zpAvQFbnP98w6W+g8DZxtjCoAewBDXOQrBUj/AXUCJ2+Ngqh3gLGNMD7f558FU/7+AT40xnYECqv892Lt+Y0zQ/wH6AVPdHt8P3G91XR7UnQ0scXu8AkhzfZ8GrLC6Rg9/jw+Ac4OxfiAamEf1WcZBUT/VJ7V9AZwNfBRs/+0AZUBSreeCon4gDijFNZElWOp3RIue+g8nDzYppvpkLlxf21hcT4NEJBs4FfieIKrf1fWxANgGfG6MCab6nwZGA8fcnguW2qH63OjPRKRYREa6nguW+tsD24Fxrq6z/4lIDDav3ylB7/Eh5Mp/RKQl8A5wtzFmn9X1eMMYc9QY04Pq1nFvEelqcUkeEZELgW3GmGKra/FBf2NMT6q7Wm8TkUFWF+SFMKAn8B9jzKnAAezWTVMHpwS9JweYB4OtIpIG4Pq6zeJ66iUi4VSH/GvGmHddTwdN/TWMMXuAr6keLwmG+vsDF4tIGTAJOFtEXiU4agfAGLPJ9XUb8B7Qm+Cpvxwod30CBHib6uC3df1OCXqnHEI+Gbje9f31VPd9246ICPAiUGKMedLtpWCpP1lEWrm+bwGcAywnCOo3xtxvjMkwxmRT/d/5l8aYawiC2gFEJEZEYmu+B84DlhAk9RtjtgAbRKST66nBwDLsXr/VgwR+HCS5AFgJrAEesLoeD+qdCGwGjlDdSrgZSKR6kG2V62uC1XXWU/sAqrvGFgELXH8uCKL6uwPzXfUvAR5yPR8U9bv9HmdyYjA2KGqnuo97oevP0pq/q8FSv6vWHkCR67+f94HWdq9ft0BQSimHc0rXjVJKqXpo0CullMNp0CullMNp0CullMNp0CullMNp0CullMNp0CullMP9P9TgDWJFu3aQAAAAAElFTkSuQmCC\n",
"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df=pd.read_csv('weight_minus.csv',skiprows=1)\nfontsize = {'fontsize': 15}\ndata=df['HEX.2'].to_numpy()\nweight_minus = []\nfor x in data:\n weight_minus.append(float(-(int(x[0],16) & 0x2) | (int(x[0],16) & 0x1)) + float(int(x[1:],16))/65536)\n #weights_update.append(float(int(x[0],16)) + float(int(x[1:],16))/65536)\nweight_minus = np.array(weight_minus)\ntime = arange(1024)*0.0625\nplt.plot(time,weight_minus)",
"execution_count": 12,
"outputs": [
{
"data": {
"text/plain": "[<matplotlib.lines.Line2D at 0x1a805581d08>]"
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Plot the results"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Visualisation\ndopamine_indices, dopamine_times = dopamine_monitor.it\nneurons_indices, neurons_times = neurons_monitor.it\nfigure(figsize=(15, 12))\nsubplot(611)\n#plot([0.05, 2.95], [2.7, 2.7], linewidth=5, color='k')\n#text(1.5, 3, 'Classical STDP', horizontalalignment='center', fontsize=20)\n#plot([3.05, 5.95], [2.7, 2.7], linewidth=5, color='k')\n#text(4.5, 3, 'Dopamine modulated STDP', horizontalalignment='center', fontsize=20)\nplot([0, simulation_duration/ms], [0,0], linewidth=1, color='0.5')\nplot([0, simulation_duration/ms], [1,1], linewidth=1, color='0.5')\nplot([0, simulation_duration/ms], [2,2], linewidth=1, color='0.5')\nplot(neurons_times/ms, neurons_indices, 'ob')\nplot(dopamine_times/ms, dopamine_indices + 2, 'or')\nxlim([0, simulation_duration/ms])\nylim([-0.5, 2.5])\nyticks([0, 1, 2], ['Pre-neuron', 'Post-neuron', 'Reward'])\nxticks([])\n\nsubplot(612)\nplot(synapse_stdp_monitor.t/ms, synapse_stdp_monitor.d.T/gmax, 'r-')\nplot(time,dopadata, '--')\nxlim([0, simulation_duration/ms])\nylabel('Extracellular\\ndopamine d(t)')\nxticks([])\n\nsubplot(613)\nplot(synapse_stdp_monitor.t/ms, synapse_stdp_monitor.c.T/gmax, 'b-')\nplot(time,e_trace, 'r--')\nxlim([0, simulation_duration/ms])\nylabel('Eligibility\\ntrace c(t)')\nxticks([])\n\nsubplot(614)\nplot(synapse_stdp_monitor.t/ms, synapse_stdp_monitor.Apre.T/gmax, 'b-')\nplot(time,weight_plus, 'r--')\nxlim([0, simulation_duration/ms])\nylabel('Apre(t)')\nxticks([])\n\nsubplot(615)\nplot(synapse_stdp_monitor.t/ms, synapse_stdp_monitor.Apost.T/gmax, 'b-')\nplot(time,weight_minus, 'r--')\nxlim([0, simulation_duration/ms])\nylabel('Apost(t)')\nxticks([])\n\nsubplot(616)\nplot(synapse_stdp_monitor.t/ms, synapse_stdp_monitor.s.T/gmax, 'g-')\nplot(time,weights_update, 'r--')\nxlim([0, simulation_duration/ms])\nylabel('Synaptic\\nstrength s(t)')\nxlabel('Time (s)')\ntight_layout()\nshow()\n#savefig('RSTDP.png')",
"execution_count": 13,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 1080x864 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Calculate the error between the simulation and the FPGA"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#plot(synapse_stdp_monitor.Apre.T[400:450], 'b-')\n#plot(weight_plus[400:450], 'r--')\n#plot((np.squeeze(synapse_stdp_monitor.c.T[1:]) - e_trace[:-65])**2)\n\nprint('Err dopa', np.sqrt(np.mean((np.squeeze(synapse_stdp_monitor.d.T) - dopadata[1:-63])**2)))\nprint('Err e_trace', np.sqrt(np.mean((np.squeeze(synapse_stdp_monitor.c.T[1:]) - e_trace[:-65])**2)))\nprint('Err weight_plus', np.sqrt(np.mean((np.squeeze(synapse_stdp_monitor.Apre.T[1:]) - weight_plus[:-65])**2)))\nprint('Err weight_minus', np.sqrt(np.mean((np.squeeze(synapse_stdp_monitor.Apost.T[1:]) - weight_minus[:-65])**2)))\nprint('Err weight', np.sqrt(np.mean((np.squeeze(synapse_stdp_monitor.s.T[1:]) - weights_update[:-65])**2)))\nprint('Valor mínimo representable: ', 2**-16)",
"execution_count": 29,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "Err dopa 3.7159529800476325e-05\nErr e_trace 0.00716249630158877\nErr weight_plus 0.001501709805521688\nErr weight_minus 0.0013671561380711324\nErr weight 0.0024260974411525167\nValor mínimo representable: 1.52587890625e-05\n"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.7.6",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"varInspector": {
"window_display": false,
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"library": "var_list.py",
"delete_cmd_prefix": "del ",
"delete_cmd_postfix": "",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"library": "var_list.r",
"delete_cmd_prefix": "rm(",
"delete_cmd_postfix": ") ",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
]
},
"gist": {
"id": "0f72b52d5b05b257e56f4ccdb6475b2a",
"data": {
"description": "Comparison of the FPGA implementation of R-STDP with a simulation done in Brian2.",
"public": false
}
},
"_draft": {
"nbviewer_url": "https://gist.github.com/0f72b52d5b05b257e56f4ccdb6475b2a"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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